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I think finding good design references is the easy part. Figuring out what to take from them is much harder.

For example, you find a dashboard with a really clean layout. But is it good because of the spacing, the information hierarchy, or simply because it has less data than your product?

I've been thinking about a few things worth checking before borrowing a design pattern:

• Context: What problem is this design solving, and is it similar to ours?

• Structure: Does the layout still work when the content gets longer or more complex?

• Interaction: What happens in empty states, errors, or when users need to go back?

• Tradeoffs: What did this product simplify or leave out to make the experience feel clean?

A new NBER paper tracks 500,000+ GitHub developers against their actual AI telemetry. The task-level gains are enormous and they mostly don't survive the trip to a shipped release. Across four app marketplaces: a sharp rise in new apps, no rise in total usage.

Most of the AI-productivity argument you've read this year is two camps shouting single numbers at each other. There's a paper out that makes both camps right and is more useful than either, and the revised version went up this month, so it's worth twenty minutes before you write your Q4 plan.

Hey all, spent today chasing a bug where Tipword would sometimes just do nothing when you hover a word. Some of you probably hit it. It was random and hard to catch, but I think I got it.

What s fixed:

• Chrome: words inside scrollable areas (like Gmail or chat apps) didn t translate at all. They do now

• Chrome: sometimes it translated the first word of the line instead of the word under your pointer. Fixed

• Buttons and labels with more than one word now translate the word you re actually pointing at, not the whole label

• The language picker closed by itself right after a fresh launch. It stays open now

We d love to hear what you think about the product.

If you have a minute, please check it out, leave an upvote, and share your feedback. Every bit of support means a lot to the team.
https://www.producthunt.com/prod...

Hey Product Hunt!

We launched ChatRecall.dev last week to stop AI context from evaporating between sessions. Since then, we ve realised the chat logs themselves hold the exact architectural blueprint developers and teams are constantly losing.

Whenever an agent builds software, it makes dozens of micro-decisions and cuts corners ("we'll fix this later") that immediately get lost once the prompt session ends.

Ever copied something, come back to it later, and had no idea what it was? A clipboard history helps, but once it holds a few hundred items it turns into a pile: a screenshot, a hash, a paragraph, another screenshot. The thing you wanted is in there, you just can't tell which one it is.

1.3.2 is about that problem.

Select any item, press Control-Shift-N, type a name and a few tags separated by commas ("wifi, office"), press Return. The name becomes the item's title in the list, so you can tell at a glance what it is. Names and tags are searchable, so "wifi" finds the Wi-Fi QR screenshot even though the image contains no such word. And an item with a name or tags is not removed by the count or age limits, so the things you keep coming back to stay put while the rest of the history keeps churning. Clear the name and tags and the item goes back to normal.

Snippets get the same tags field. If you had a keyword on a snippet, it is now a tag.

Vitals is live on Product Hunt today. It's an Activity Monitor alternative for Mac that shows you 60 apps instead of 1,000 processes, and in its first week, close to 830 licenses have sold. This is the story of why I built it, how it works, and why I think it's the best way to see what your Mac is actually doing.

I was scrolling through X today when one of my X friends shared screenshots of two employees who recently moved from Anthropic to OpenAI.

We had an era when people were leaving OpenAI for Anthropic in large numbers, Sam Altman was becoming the second most-hated Mark Zuckerberg, and Anthropic was attracting talent from OpenAI.

Now the direction seems to be changing.

You can now connect content sources to your localization engines in Studio, translate content with your team's glossary, rules, and brand voice, and write translations back to your repositories, CMSs, or help centers.

Connect GitHub, Payload CMS, or Zendesk Help Center, or submit content through the CLI and API. Each source uses the localization engine you choose, with your team's glossary, rules, and brand voice. Product and content teams submit content from their own tools, while the localization team runs the localization infrastructure.

The CLI. Engineers use lingo push to submit source files through the localization engine's configured pipeline. lingo pull writes the completed translations to local target files on the same machine and checkout. Studio lists each submission with its status by language.

The GitHub App. When source files change on the default branch, the app translates the changes and opens a pull request with the translated files. Enable the pull request workflow to commit translations directly to a pull request branch. Require approval before automatic runs, or use /lingo translate to request specific files and languages.

Your app is online, the main flow works, and you want to step away for the weekend.

What would you check before closing the laptop?

So, been running a AI startup for more than a year now and the one thing i always thought or was puzzled was when naturally customers think AI should be cheaper than human hours even though AI's throughtout or the benefits is 100x.
So we have been doing it the 'cost+' pricing way, but to be very frank this is working and is helping us be in control of the margins, but also exploring outcome based pricing in certain use cases. Will have this thread open to have discussions around this topic, also if i find newer ways or use cases where outcome based pricing works, happy to explore.

The supply-chain scan used to work one repository at a time, from the dashboard. If a worm had pushed an implant into one of your 90 repos, you'd have had to open them one by one to find out.

Now one command does the round:

Whether you upvoted, commented, shared the launch, tried the product, or gave us feedback.

Getting tiun in front of thousands of founders and builders and finishing #2 for the entire month means a lot to us.

We're still early, and we're just getting started.

End of the week means looking back at what I planned vs what I actually did.

What tasks were completed, what wasn t, and what needs to move to next week.

There are plenty of event calendars out there, but most just tell you when and where something is happening.

As a founder, that's only half the information you actually need.

What I wanted to know was:

• Which investors actually attend?

• Is the event relevant to my startup's stage and industry?

• Can I apply to pitch, and what could I win?

• Are there discounted or free startup passes?

• Is it worth spending a few days and potentially thousands of euros attending?

Hey guys, super quick announcement ahead of tomorrow's launch - we've added MCP servers!
#1: Trophy Docs MCP
The Docs MCP gives agents always-up-to-date context on Trophy's features, integrations and guides, reducing hallucinations and enforcing best practices.
Ask your agent things like:
"What Trophy features should I use to increase user engagement?"
"How should I use Trophy to build habitual usage patterns?"
"Does Trophy support streak freezes?"
#2: Trophy Account MCP
The Account MCP gives AI agents access to your Trophy account helping you configure and deploy gamification features faster, without touching the dashboard.
Ask your agent things like:
"Set up a daily streak in Trophy that tracks completed workouts, give users 1 freeze to start with, and 1 new freeze every week. Set up a webhook endpoint to send users a guest pass when they hit a 30-day streak."
"Set up a repeating weekly leaderboard that ranks the top 100 users by XP. Award XP for reading notes, flipping flashcards and watching videos. Add a 2X XP boost on weekends for Pro users only."
"Create a series of achievements for 10, 25, 50, 100 donations. Reward users with increasing XP for hitting each level."

Get Connected

Connect Trophy's MCP servers to your favourite AI tools including Claude, ChatGPT, Cursor, Claude Code, Codex and any other AI client that supports the MCP protocol:

- Docs MCP: `https://docs.trophy.so/mcp`
- Account MCP: `https://mcp.trophy.so/mcp` (Trophy Account required)

Hey everyone,

Thank you for the upvotes, comments and questions on Ghostifier's launch, you all helped this launch exceed all of my expectations. We finished #9 for the day!

As teams ship faster with AI coding assistants (Claude Code, Cursor, etc.), the volume of PRs has skyrocketed. But the time engineers spend reviewing code hasn't scaled at all.

I've seen two sides to this lately:

1. Teams where PRs sit in queue for 2 3 days, creating a massive pipeline bottleneck.

2. Teams trying AI reviewers, but ending up with automated spam comments that nobody reads.

I m curious how your team handles this:

Hey everyone

We've been working hard on ShellMate, and we've made some big improvements in v3.

WeftCut opens in A/B Roll mode by default. The name comes from filmmaking, where A-roll carries the main story and B-roll provides supporting footage. You start with the main story, and then add some additional content over it. Making videos is just a thing like that.

The problem is that the timeline in an editor is too complicated. Some scenes need subtitles, some layers are related to each other, and some parts need an extra image, for example. Each addition has a purpose. As tracks accumulate, the timeline starts demanding more attention: scrolling vertically, finding the right clip, checking what sits above what.

Everyone keeps saying AI finally levels the field for small teams and solo founders. I m not so sure. The tools help, but there s only so much one or two people can do in a day, especially when you re also handling all the parts that aren t building. Bigger teams still out-post, out-ship, and out-distribute you. AI gave everyone the same boost, so I don t think the gap actually closed.

For other small teams: has AI really made it easier to keep up, or does it still feel the same?

WOW Our first users were cooking since launch!

Below you can find some pictures of the products created during the last week of testing and the first days of our launch, and the outcome is amazing!

Check out the full 3D models, including the B-rep data, in the Library on our website!

But there is more. Autonomyware creates market-ready products. This includes the requirements, the risk analysis, the architecture, and everything a world-class engineering team would create.

A model has read your documents. Three agents fork that working memory and pursue different reasoning paths. One calls a tool and takes in new evidence while the others keep generating.

What does that look like under the hood?

CipherLock is going on hold for a while, and I wanted to say it here instead of just going quiet.

This week I started a software development internship, on top of my university courses and my undergraduate thesis. Right now I can't give the project the time it deserves, and I'd rather pause it honestly than keep it half alive. My thesis is on lightweight cryptography, so I'm not leaving the subject, just this project for a bit.

Yesterday (September 28th), we launched MCP Connectors by Databox, and you pushed it all the way to #1 Product of the Day. It's our first #1, and we couldn't have done it without you.

Huge thanks to the Databox team and to everyone here who upvoted, commented, asked the hard questions, and connected their first tool. You helped us confirm something we'd suspected for a while: knowing that a number moved isn't enough. People want to know why.

A few things we heard loud and clear: